




'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   "▁"

UpperCAmelCase_					=   {"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

UpperCAmelCase_					=   {
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
UpperCAmelCase_					=   ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   ["""input_ids""", """attention_mask"""]

   _a    						=   []
   _a    						=   []







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="</s>"   ,				UpperCamelCase="</s>"   ,				UpperCamelCase="<s>"   ,				UpperCamelCase="<unk>"   ,				UpperCamelCase="<pad>"   ,				UpperCamelCase="<mask>"   ,				UpperCamelCase = None   ,				**UpperCamelCase   ,				)					->     None:
     # Mask token behave like a normal word, i.e. include the space before it
     __a								=       AddedToken(UpperCamelCase   ,				lstrip=UpperCamelCase   ,				rstrip=UpperCamelCase   ) if isinstance(UpperCamelCase   ,				UpperCamelCase   ) else mask_token

     __a								=       {} if sp_model_kwargs is None else sp_model_kwargs

     __a								=       kwargs.get('additional_special_tokens'   ,				[]   )
     kwargs["additional_special_tokens"] += [
         code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         src_lang=UpperCamelCase   ,				tgt_lang=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				sep_token=UpperCamelCase   ,				cls_token=UpperCamelCase   ,				pad_token=UpperCamelCase   ,				mask_token=UpperCamelCase   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**UpperCamelCase   ,				)

     __a								=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(str(UpperCamelCase   )   )
     __a								=       vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # Mimic fairseq token-to-id alignment for the first 4 token
     __a								=       {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     __a								=       1

     __a								=       len(self.sp_model   )
     __a								=       {
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase   )
     }
     __a								=       {v: k for k, v in self.lang_code_to_id.items()}
     __a								=       len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
     __a								=       {v: k for k, v in self.fairseq_tokens_to_ids.items()}

     __a								=       src_lang if src_lang is not None else 'en_XX'
     __a								=       self.lang_code_to_id[self._src_lang]
     __a								=       tgt_lang
     self.set_src_lang_special_tokens(self._src_lang   )







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset + 1  # Plus 1 for the mask token







   @property
   def 		UpperCamelCase__      (						self   )					->     str:
     return self._src_lang







   @src_lang.setter
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       new_src_lang
     self.set_src_lang_special_tokens(self._src_lang   )







   def __getstate__(						self   )					->     Dict:
     __a								=       self.__dict__.copy()
     __a								=       None
     return state







   def __setstate__(						self   ,				UpperCamelCase   )					->     None:
     __a								=       d

     # for backward compatibility
     if not hasattr(self   ,				'sp_model_kwargs'   ):
       __a								=       {}

     __a								=       spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     __a								=       {self.convert_ids_to_tokens(UpperCamelCase   ): i for i in range(self.vocab_size   )}
     vocab.update(self.added_tokens_encoder   )
     return vocab







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     List[str]:
     return self.sp_model.encode(UpperCamelCase   ,				out_type=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     int:
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __a								=       self.sp_model.PieceToId(UpperCamelCase   )

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     str:
     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       []
     __a								=       ''
     __a								=       False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(UpperCamelCase   ) + token
         __a								=       True
         __a								=       []
       else:
         current_sub_tokens.append(UpperCamelCase   )
         __a								=       False
     out_string += self.sp_model.decode(UpperCamelCase   )
     return out_string.strip()







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     Tuple[str]:
     if not os.path.isdir(UpperCamelCase   ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"   )
       return
     __a								=       os.path.join(
         UpperCamelCase   ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

     if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCamelCase   ) and os.path.isfile(self.vocab_file   ):
       copyfile(self.vocab_file   ,				UpperCamelCase   )
     elif not os.path.isfile(self.vocab_file   ):
       with open(UpperCamelCase   ,				'wb'   ) as fi:
         __a								=       self.sp_model.serialized_model_proto()
         fi.write(UpperCamelCase   )

     return (out_vocab_file,)







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   ,				UpperCamelCase = False   )					->     List[int]:

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=UpperCamelCase   ,				token_ids_a=UpperCamelCase   ,				already_has_special_tokens=UpperCamelCase   )

     __a								=       [1] * len(self.prefix_tokens   )
     __a								=       [1] * len(self.suffix_tokens   )
     if token_ids_a is None:
       return prefix_ones + ([0] * len(UpperCamelCase   )) + suffix_ones
     return prefix_ones + ([0] * len(UpperCamelCase   )) + ([0] * len(UpperCamelCase   )) + suffix_ones







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     Union[str, Any]:
     if src_lang is None or tgt_lang is None:
       raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
     __a								=       src_lang
     __a								=       self(UpperCamelCase   ,				add_special_tokens=UpperCamelCase   ,				return_tensors=UpperCamelCase   ,				**UpperCamelCase   )
     __a								=       self.convert_tokens_to_ids(UpperCamelCase   )
     __a								=       tgt_lang_id
     return inputs







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = "en_XX"   ,				UpperCamelCase = None   ,				UpperCamelCase = "ro_RO"   ,				**UpperCamelCase   ,				)					->     BatchEncoding:
     __a								=       src_lang
     __a								=       tgt_lang
     return super().prepare_seqaseq_batch(UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     return self.set_src_lang_special_tokens(self.src_lang   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     return self.set_tgt_lang_special_tokens(self.tgt_lang   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       self.lang_code_to_id[src_lang]
     __a								=       [self.cur_lang_code_id]
     __a								=       [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     None:
     __a								=       self.lang_code_to_id[tgt_lang]
     __a								=       [self.cur_lang_code_id]
     __a								=       [self.eos_token_id]









'''simple docstring'''


from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_					=   {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

UpperCAmelCase_					=   {
    "facebook/blenderbot_small-90M": 5_12,
}





class  __lowercase    (	__magic_name__			):
   _a    						=   VOCAB_FILES_NAMES
   _a    						=   PRETRAINED_VOCAB_FILES_MAP
   _a    						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a    						=   BlenderbotSmallTokenizer







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase="<|endoftext|>"   ,				UpperCamelCase=False   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     List[Any]:
     super().__init__(
         ByteLevelBPETokenizer(
             vocab=UpperCamelCase   ,				merges=UpperCamelCase   ,				add_prefix_space=UpperCamelCase   ,				trim_offsets=UpperCamelCase   ,				)   ,				bos_token=UpperCamelCase   ,				eos_token=UpperCamelCase   ,				unk_token=UpperCamelCase   ,				**UpperCamelCase   ,				)
     __a								=       add_prefix_space







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=None   )					->     List[Any]:
     __a								=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
       return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = None   )					->     List[int]:
     __a								=       [self.sep_token_id]
     __a								=       [self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep   ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]



